Accurate classification of frost thickness using visual information in a domestic refrigerator.

Due to the intensive use of the domestic refrigerator based on vapor compression and the particular design of the no-frost type, it is essential to propose economic and light strategies to optimize the refrigerator's energy consumption. When a large amount of frost accumulates on the evaporator, heat exchange decreases, affecting the cooling capacity. The amount of frost accumulation on the evaporator surface plays a critical role in optimizing frost removal. For this reason, this paper presents a novel method that uses visual information to classify the frost accumulated on the evaporator surface accurately. This method has two parts: a data acquisition system based on an RGB sensor and an intelligent model that processes the visual information and classifies the frost accumulated on the evaporator surface. The data acquisition system creates the datasets for the training and validation of the intelligent model. This smart model is based on convolutional neural networks. Many tests were performed on a pair of different domestic refrigerators using typical usage habits for data collection. Each test was performed continuously for 24 h, emulating real-life usage. One of the main contributions of this work is a new, accurate, and low-cost method to classify the amount of frost accumulated on the evaporator surface. Furthermore, one advantage of the proposed method is the ease of integrating into any domestic refrigerator. The results indicate that this proposed method can classify with an accuracy as high as 99.75 % four different levels of frost accumulated on the evaporator surface of a domestic refrigerator. [ABSTRACT FROM AUTHOR]